Battery Life Prediction Using Sparse Ridge Regression Techniques

Abstract

Battery life prediction has become a crucial component in the design and management of modern electronic systems, particularly for mobile devices, electric vehicles, and IoT applications. With the advent of data-driven modeling and machine learning, predictive maintenance and lifecycle estimation have taken center stage in the domain of energy systems. This research explores the effectiveness of Sparse Ridge Regression (SRR) in predicting battery lifespan by leveraging high-dimensional feature sets and controlling overfitting through L2 regularization combined with sparsity-inducing techniques. Unlike traditional ridge regression, SRR not only addresses multicollinearity but also introduces feature selection capabilities, thereby enhancing model interpretability and computational efficiency. We have employed real-world datasets from lithium-ion battery usage scenarios under varying charge/discharge conditions. The study includes preprocessing steps, feature engineering, model training, hyperparameter tuning, and evaluation through multiple metrics such as RMSE, MAE, and R² score. The experimental results demonstrate the superior performance of SRR over baseline models, including linear regression and LASSO, in terms of both accuracy and generalization. The findings highlight the potential of SRR in real-time battery health monitoring systems and provide a foundation for deploying predictive models in resource-constrained environments.